The field of cybersecurity is moving towards the development of more sophisticated anomaly detection systems, particularly in the context of complex and imbalanced datasets. Researchers are exploring the use of semi-supervised learning techniques, sequential modeling, and hybrid deep learning approaches to improve the detection of anomalies and threats. These innovative methods aim to address the challenges of class imbalance, label scarcity, and complex patterns in various domains, including cybersecurity and healthcare. The use of transfer learning and domain-adversarial mechanisms is also being investigated to enhance the robustness and generalization performance of anomaly detection models. Noteworthy papers in this area include:

• A study that proposes a User-Based Sequencing methodology and achieves state-of-the-art performance in insider threat detection with 96.61% accuracy and 99.43% recall.
• A paper that explores a hybrid deep learning approach for anomaly detection in mental healthcare provider billing, combining Long Short-Term Memory networks and Transformers with pseudo-labeling via Isolation Forests and Autoencoders.
• A research that introduces a domain-adversarial transfer learning method for fault root cause identification in cloud computing systems, demonstrating stronger discriminative power and robustness under extreme class imbalance and heterogeneous node environments.